Tunnelling and Underground Space Technology最新文献_第5页

Deep-learning-based longitudinal joint opening detection method for metro shield tunnel 基于深度学习的地铁盾构隧道纵向接头开口检测方法

IF 6.7 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2024-10-14 DOI: 10.1016/j.tust.2024.106108

In this paper, a longitudinal joint opening detection method using a precise longitudinal segment joint extraction algorithm featuring deep neural networks (DNNs) is proposed. The proposed method consists of the following four steps. First, a mobile scanning system is employed to obtain three-dimensional metro shield tunnel point clouds. Then, two small DNNs, YOLOv5 and JLNet, were designed to accurately extract the longitudinal segment joint lines from the images generated from the scanned point clouds. YOLOv5 rapidly detects the approximate longitudinal segment joint areas, while JLNet precisely fits the joint lines. Subsequently, using the extracted segment joint lines, the points associated with different tunnel segments can be segmented accordingly. Finally, based on the tunnel segment point clouds, a joint opening angle calculation method that combines the cylinder projection and plane-fitting algorithms is proposed. Experimental results demonstrate that the proposed DNN-based method can accurately extract segment joint lines without being influenced by the tunnel equipment around the segment joints. The YOLOv5 network exhibited a classification accuracy of 0.9907 and a bounding box prediction error of 0.004. For the JLNet network, the line slope prediction error was 0.0072, with an intercept error of 1.53 pixels. The joint opening spatial distribution pattern was identified by comparing the joint opening angles in the deformed and undeformed tunnels. Additionally, the accuracy of the proposed method was evaluated, revealing that the joint opening angle detection external accuracy was 0.13°.

本文提出了一种纵向关节开口检测方法，该方法采用以深度神经网络（DNN）为特征的精确纵向节段关节提取算法。该方法包括以下四个步骤。首先，采用移动扫描系统获取三维地铁盾构隧道点云。然后，设计了两个小型 DNN（YOLOv5 和 JLNet），用于从扫描点云生成的图像中精确提取纵向节段连接线。YOLOv5 能快速检测出近似的纵向节段连接区域，而 JLNet 则能精确拟合连接线。随后，利用提取的节段连接线，可以相应地分割出与不同隧道节段相关的点。最后，基于隧道段点云，提出了一种结合圆柱体投影和平面拟合算法的接头开口角计算方法。实验结果表明，所提出的基于 DNN 的方法可以准确提取分段连接线，而不受分段连接线周围隧道设备的影响。YOLOv5 网络的分类精度为 0.9907，边界框预测误差为 0.004。JLNet 网络的线路斜率预测误差为 0.0072，截距误差为 1.53 像素。通过比较变形隧道和未变形隧道的接头开口角度，确定了接头开口的空间分布模式。此外，还对所提方法的精度进行了评估，结果表明接头开口角度检测的外部精度为 0.13°。

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引用次数: 0

Evaluating the impact of tunnel slope on critical velocity and confinement velocity in metro tunnel carriage fires 评估隧道坡度对地铁隧道车厢火灾临界速度和限制速度的影响

IF 6.7 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2024-10-14 DOI: 10.1016/j.tust.2024.106141

Smoke control within metro tunnels stands as a pivotal measure in ensuring both the orderly evacuation and safety of passengers. This study investigated the critical velocity and confinement velocity in inclined metro tunnel fires via model-scale experiments. The tunnel slope varied from 2 % ∼ 8 % and four fire heat release rates of 2.29 kW, 3.44 kW, 4.59 kW and 5.74 kW. Results show that the confinement velocity and critical velocity monotonically increase with the heat release rate and tunnel slope. The confinement velocity V_conf was notably affected by the equivalent fire source Q_u. The natural exponential function could effectively express the relationship between Q* and Q_u*, with the correction factor of tunnel slope utilized to adjust the expression. A new model for critical velocity and confinement velocity was characterized by considering the tunnel slope and heat release rate. Comparative results indicated that the optimizing confinement velocity was significantly lower than that defined in previous studies, while the proposed critical velocity ratio aligns well with findings from prior research. This study provides valuable insights into the design of smoke control systems customized for fires occurring in trains immobilized within inclined underground tunnels.

地铁隧道内的烟雾控制是确保乘客有序疏散和安全的关键措施。本研究通过模型实验研究了倾斜地铁隧道火灾的临界速度和封闭速度。隧道坡度为 2 % ～ 8 %，火灾热释放率分别为 2.29 kW、3.44 kW、4.59 kW 和 5.74 kW。结果表明，封闭速度和临界速度随着热释放率和隧道坡度的增加而单调增加。封闭速度 Vconf 受到等效火源 Qu 的显著影响。自然指数函数可以有效地表达 Q* 和 Qu* 之间的关系，并利用隧道坡度的修正系数来调整表达式。通过考虑隧道坡度和热释放率，建立了一个新的临界速度和限制速度模型。比较结果表明，优化的封闭速度明显低于之前研究中定义的速度，而提出的临界速度比与之前的研究结果非常吻合。这项研究为设计专门针对固定在倾斜地下隧道内的列车火灾的烟雾控制系统提供了宝贵的见解。

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引用次数: 0

New limit states for the seismic fragility assessment of circular tunnels: Application in case of tunnels in clayey soil deposits 圆形隧道地震脆性评估的新极限状态：在粘性土隧道中的应用

IF 6.7 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2024-10-14 DOI: 10.1016/j.tust.2024.106129

This paper introduces a novel framework to define limit states for the seismic fragility assessment of circular tunnels in soil. A numerical framework is developed for this purpose, focusing on the response of tunnels subjected to ground seismic shaking in the transverse direction. New limit states are defined based on the ovaling deformation of the tunnel, corresponding to different levels of liner stiffness degradation caused by seismic shaking. The latter is evaluated via nonlinear static pushover analyses of the examined ground-tunnel configurations. Nonlinear dynamic analyses are performed to evaluate the demand of examined tunnels and develop Probabilistic Seismic Demand Models (PSDMs). The uncertainties related with the definitions of capacity and demand are thoroughly evaluated based on the results of the nonlinear static pushover and dynamic analyses, respectively. The proposed framework is applied to a 6 m diameter circular tunnel embedded in uniform clayey soil deposit at a burial depth of 15 m. Various assumptions are made regarding the thickness and mechanical properties of the liner and the soil, leading to the investigation of 27 ground-tunnel configurations. A suite of ground motions is selected to perform dynamic analyses of each examined configuration. Based on the results of the analyses new PSDMs and PGA-based fragility functions are derived. Comparisons of the proposed fragility curves with existing, empirical, and analytical fragility curves for tunnels, reveal differences, which in some cases are significant and are mainly attributed to the different definitions of Engineering Demand Parameters (EDPs) and limit states between the compared curves, as well as to different assumptions in the analytical frameworks proposed by various studies. The proposed framework may be applied to other ground-tunnel configurations to develop fragility functions for a more rigorous risk and resilience assessment of these types of systems.

本文介绍了一种新的框架，用于定义土壤中圆形隧道地震脆性评估的极限状态。为此，本文开发了一个数值框架，重点关注隧道在横向地震动下的响应。根据隧道的椭圆形变形定义了新的极限状态，与地震震动引起的不同程度的衬砌刚度退化相对应。后者是通过对所考察的地面隧道配置进行非线性静态推移分析来评估的。非线性动态分析用于评估受检隧道的需求，并建立概率地震需求模型（PSDM）。根据非线性静态推移分析和动态分析的结果，分别对与容量和需求定义相关的不确定性进行了全面评估。提出的框架适用于一个直径 6 米、埋深 15 米、嵌入均匀粘土沉积层的圆形隧道。对衬砌和土壤的厚度及机械性能做了各种假设，从而对 27 种地面隧道配置进行了研究。我们选择了一套地动图，对每种检查过的构造进行动态分析。根据分析结果，得出了新的 PSDM 和基于 PGA 的脆性函数。将所提出的脆性曲线与现有的、经验的和分析的隧道脆性曲线进行比较，发现两者之间存在差异，在某些情况下差异很大，这主要归因于所比较的曲线之间工程需求参数（EDPs）和极限状态的定义不同，以及各种研究提出的分析框架中的假设不同。建议的框架可用于其他地面隧道配置，以开发脆性函数，对这些类型的系统进行更严格的风险和复原力评估。

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引用次数: 0

Effect of borehole deviation on raise formation via long-hole raise blasting 钻孔偏差对通过长孔提升爆破形成提升的影响

IF 6.7 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2024-10-14 DOI: 10.1016/j.tust.2024.106136

During Long-hole raises blasting (LHRB) in underground mines, borehole deviation seriously affects the blasting effect, greatly increases the difficulty of raise formation, and reduces the quality of formed raises. To study the effect of borehole deviation on raise formation via burn cuts blasting mode (BCBM), this study theoretically analysed the effect of borehole deviation on raise formation via blasting, constructed and validated a mechanism model of underground deep hole deviation, revealed the effect pattern of borehole deviation on the formation of Long-hole raises via BCBM under different cut modes, and guided the practice of raise formation via deep hole blasting in mines. The results showed that (1) deviation-inducing rock strata with alternating soft and hard rocks are important causes of borehole deviation in deep holes; (2) under the same geological conditions, the borehole deviation is usually negatively correlated with the borehole diameter; (3) the resistance to borehole deviation of the four-empty-hole barrel cut mode (FECM) is better than that of the single-empty-hole spiral cut mode (SECM),and (4) optimizing the firing order of blasting based on the actual measurement of borehole deviation can significantly improve the quality of raise formation. The results of this study provide a basis for assessing the mechanism of Long-hole deviation and theoretical and practical references for the design of LHRB under the condition of borehole deviation.

在地下矿山长孔提升爆破（LHRB）过程中，钻孔偏斜严重影响爆破效果，大大增加了提升形成的难度，降低了形成提升的质量。为研究井眼偏差对烧切爆破（BCBM）形成隆起的影响，本研究从理论上分析了井眼偏差对爆破形成隆起的影响，构建并验证了井下深孔偏差机理模型，揭示了不同烧切模式下井眼偏差对BCBM形成长孔隆起的影响规律，指导了矿井深孔爆破形成隆起的实践。结果表明：（1）软硬交替的诱发偏差岩层是深孔钻孔偏差的重要原因；（2）在相同地质条件下，钻孔偏差通常与钻孔直径呈负相关；(3）四空孔筒形切削模式（FECM）的抗钻孔偏差能力优于单空孔螺旋切削模式（SECM）；以及（4）根据钻孔偏差的实际测量结果优化爆破的起爆顺序，可显著提高养生层的质量。本研究的结果为评估长孔偏差的机理提供了依据，并为孔偏差条件下的 LHRB 设计提供了理论和实践参考。

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引用次数: 0

Unreinforced construction techniques: ECC-based unreinforced shield tunnel segment joints for enhancing underground infrastructure resilience 非加固施工技术：基于 ECC 的非加固盾构隧道节段接缝用于增强地下基础设施的复原力

IF 6.7 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2024-10-14 DOI: 10.1016/j.tust.2024.106119

The development of unreinforced construction techniques for shield tunnel segments is crucial for enhancing resilience of underground infrastructure, but research in this field is still lacking. This study addresses this gap by conducting full-scale experiments on ECC-based unreinforced shield segment joints (ECCUS) and comparing their performance with traditional reinforced concrete segment joints (RCS) and ECC reinforced segment joints (ECCRS) under extreme bending loads. The research focuses on material characteristics, segmental deflection, joint behavior, bolt strain, damage propagation, failure modes, joint toughness, and ductility. The results revealed that ECCUS joints had a bearing capacity 2.64 times that of RCS and 1.32 times that of ECCRS in the elastic phase. Their ultimate load capacity surpassed RCS by 27.4% and ECCRS by 24.4%. ECCUS also demonstrated superior ductility, with increases of 131% over ECCRS and 78% over RCS, and exhibited finer, more numerous cracks, enhancing energy absorption and deformability. ECCUS bolts showed a 24% reduction in average strain and a 74% decrease in strain deviation compared to RCS and ECCRS. Furthermore, ECCUS joints displayed exceptional toughness, being 6.2 times greater than RCS and 1.5 times higher than ECCRS during normal serviceability. These findings underscore the potential of ECC in improving the performance and durability of unreinforced tunnel segments.

开发盾构隧道段非加固施工技术对于增强地下基础设施的韧性至关重要，但这一领域的研究仍然缺乏。本研究针对这一空白，对基于 ECC 的非加固盾构分段接头（ECCUS）进行了全尺寸实验，并比较了其与传统钢筋混凝土分段接头（RCS）和 ECC 加固分段接头（ECCRS）在极端弯曲荷载下的性能。研究重点包括材料特性、分段挠度、连接行为、螺栓应变、损伤扩展、失效模式、连接韧性和延展性。研究结果表明，在弹性阶段，ECCUS 接头的承载能力是 RCS 的 2.64 倍，是 ECCRS 的 1.32 倍。其极限承载能力比 RCS 高 27.4%，比 ECCRS 高 24.4%。ECCUS 还表现出卓越的延展性，比 ECCRS 提高了 131%，比 RCS 提高了 78%，并表现出更细更多的裂纹，增强了能量吸收和变形能力。与 RCS 和 ECCRS 相比，ECCUS 螺栓的平均应变降低了 24%，应变偏差降低了 74%。此外，ECCUS 接头显示出卓越的韧性，在正常使用期间，其韧性是 RCS 的 6.2 倍，是 ECCRS 的 1.5 倍。这些发现凸显了 ECC 在改善非加固隧道节段的性能和耐久性方面的潜力。

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引用次数: 0

Instability mechanism and reinforcement measures for segments of Ultra-Large diameter shield tunnels when constructing cross passages by mechanical methods 超大直径盾构隧道在采用机械方法建造穿越通道时的失稳机理和分段加固措施

IF 6.7 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2024-10-10 DOI: 10.1016/j.tust.2024.106125

Construction of cross passages by mechanical methods represents an emerging construction technique with numerous advantages. However, cutting through segments of ultra-large diameter (ULD) shield tunnels poses significant safety risks, and the instability mechanism of those structures with lateral openings remains unclear. Based on China’s first ULD shield tunnel project that adopted the mechanical method for construction of cross passages, refined three-dimensional finite element models were created with ABAQUS. The models considered material and geometric non-linearity and were employed for comprehensive analyses of construction disturbance responses, instability mechanisms of segments, and reinforcement measures. The findings reveal that (1) mechanical construction of cross passages leads to a 24.84 % to 58.57 % increase in structural convergence deformation and a significant stress concentration; (2) The C-shaped ring and the semi-split rings no longer behave consistently in terms of force and deformation after the opening is formed. Additionally, there are significant shifts in the instability characteristics under localized surcharge loads of the segmental lining, resulting in a 30 % decrease in instability start load; (3) After adopting the reinforcement measure of steel ring girders (SRG), the instability process of the structure with opening is similar to that of the initial structure. The loss in instability start load, caused by mechanical construction, has been reduced by 34.4 % ∼ 84.4 %. However, increasing SRG thickness beyond 40 mm has a limited impact on reinforcement efficacy. This study offers theoretical and practical insights for similar engineering projects, positively contributing to project safety.

用机械方法建造横向通道是一种新兴的施工技术，具有诸多优点。然而，超大直径（ULD）盾构隧道的分段掘进会带来巨大的安全风险，而且横向开口结构的失稳机理尚不清楚。基于中国首个采用机械法建造横向通道的超大直径盾构隧道项目，利用 ABAQUS 建立了精细的三维有限元模型。模型考虑了材料和几何非线性因素，用于综合分析施工扰动响应、分段失稳机理和加固措施。研究结果表明：（1）交叉通道的机械施工会导致结构收敛变形增加 24.84% 至 58.57%，并出现明显的应力集中；（2）开口形成后，C 形环和半剖环在力和变形方面的表现不再一致。此外，分段衬砌在局部附加荷载作用下的失稳特性也发生了显著变化，导致失稳起始荷载降低了 30%；（3）采用钢环梁（SRG）加固措施后，开口结构的失稳过程与初始结构类似。机械施工造成的失稳起始荷载损失减少了 34.4% ∼ 84.4%。然而，将 SRG 厚度增加到 40 毫米以上对加固效果的影响有限。这项研究为类似工程项目提供了理论和实践启示，对项目安全起到了积极作用。

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引用次数: 0

Data-driven prediction model for the heat performance of energy tunnels 能源隧道热性能数据驱动预测模型

IF 6.7 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2024-10-10 DOI: 10.1016/j.tust.2024.106127

To date, it has been challenging to quickly and accurately quantify the heat performance of energy tunnels under unknown conditions. This study innovatively introduces an intelligent prediction model for the thermal performance of energy tunnels to address the above difficulties. Five machine learning (ML) prediction models for energy tunnel heat flux were established based on a database sourced from various regions, various conditions, and various operations. The prediction results were compared with the measured heat flux of the energy tunnel to determine the prediction performance of these ML models, and the sensitivity of the input variables was also analysed. The results indicate that the established database has reliable representation, as the selected variables (features) in this database are independent and relatively random. Furthermore, these ML models can accurately capture the trends of energy tunnel heat flux values under unknown conditions, with the random forest model demonstrating the best prediction performance, generalization ability, and great accuracy among these five ML models. These 14 input variables in the database are categorized into three groups according to the sensitivity analysis: thermal variables, design variables, and other variables (environmental and test variables). These findings provide confidence for the intelligent prediction of energy tunnel heat performance.

迄今为止，在未知条件下快速准确地量化能源隧道的热性能一直是一项挑战。本研究创新性地引入了能源隧道热性能智能预测模型，以解决上述难题。基于来自不同地区、不同条件和不同运营的数据库，建立了五个能源隧道热通量的机器学习（ML）预测模型。将预测结果与能源隧道的实测热通量进行比较，以确定这些 ML 模型的预测性能，并分析了输入变量的敏感性。结果表明，所建立的数据库具有可靠的代表性，因为该数据库中选择的变量（特征）是独立且相对随机的。此外，这些 ML 模型能够准确捕捉未知条件下能量隧道热通量值的变化趋势，其中随机森林模型在这五个 ML 模型中表现出最佳的预测性能、泛化能力和高准确度。根据灵敏度分析，数据库中的 14 个输入变量被分为三类：热变量、设计变量和其他变量（环境变量和测试变量）。这些研究结果为能源隧道热能性能的智能预测提供了信心。

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引用次数: 0

Automatic detection of tunnel lining crack based on mobile image acquisition system and deep learning ensemble model 基于移动图像采集系统和深度学习集合模型的隧道衬砌裂缝自动检测

IF 6.7 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2024-10-10 DOI: 10.1016/j.tust.2024.106124

Tunnel cracks are a crucial indicator of tunnel detection and performance evaluation. However, traditional manual inspection methods are time-consuming and dangerous. To address these problems, an automatic tunnel crack detection method based on a mobile image acquisition system and deep learning ensemble model is proposed. A novel mobile image acquisition system is proposed for tunnel data acquisition. A deep learning-based model, named You Only Look Once v8 enhanced by large separable kernel attention (LSKA) and dynamic snake convolution (DSC; YOLO-LD), is proposed to improve the crack detection performance. Collaborative learning is used to combine the YOLO-LD object detection and semantic segmentation models into an ensemble model to enhance the model’s engineering adaptability. Edge computing technologies are used for ensemble model deployment and inference acceleration. The method is tested on the custom tunnel lining crack (TL-Crack), the open-access dataset LinkCrack, and highway tunnel field data. The results show that the mobile image acquisition system can rapidly acquire high-resolution images and form panoramic images. The YOLO-LD model outperforms other state-of-the-art models in terms of precision, recall, and F1-score on both TL-Crack and LinkCrack. The ensemble model fully exploits the YOLO-LD object detection model’s crack localization capability and the YOLO-LD semantic segmentation model’s crack extraction performance, improving the model’s engineering adaptability. Edge computing techniques increase the inference speed of the ensemble model to 84 images/second. Parameters such as stake number, distribution, length, width, and type of cracks are calculated, and the crack distribution maps are prepared to assist inspectors in field verification.

隧道裂缝是隧道检测和性能评估的重要指标。然而，传统的人工检测方法既耗时又危险。针对这些问题，本文提出了一种基于移动图像采集系统和深度学习集合模型的隧道裂缝自动检测方法。本文提出了一种用于隧道数据采集的新型移动图像采集系统。为提高裂缝检测性能，提出了一种基于深度学习的模型，命名为 "你只看一次 v8"，该模型通过大可分离内核注意（LSKA）和动态蛇卷积（DSC；YOLO-LD）进行了增强。协作学习用于将 YOLO-LD 物体检测模型和语义分割模型组合成一个集合模型，以增强模型的工程适应性。边缘计算技术用于集合模型的部署和推理加速。该方法在定制隧道衬砌裂缝（TL-Crack）、开放数据集 LinkCrack 和高速公路隧道现场数据上进行了测试。结果表明，移动图像采集系统可以快速获取高分辨率图像并形成全景图像。在 TL-Crack 和 LinkCrack 数据集上，YOLO-LD 模型在精确度、召回率和 F1 分数方面均优于其他先进模型。集合模型充分利用了 YOLO-LD 物体检测模型的裂纹定位能力和 YOLO-LD 语义分割模型的裂纹提取性能，提高了模型的工程适应性。边缘计算技术将集合模型的推理速度提高到每秒 84 幅图像。计算出裂缝的桩数、分布、长度、宽度和类型等参数，并绘制出裂缝分布图，以协助检测人员进行现场核查。

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引用次数: 0

“Cosmopolitan Canopy” in Hong Kong’s hidden city: Urban design and social life in underground transit hub 香港隐秘城市的 "大都会天幕"：地下交通枢纽的城市设计与社会生活

IF 6.7 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2024-10-10 DOI: 10.1016/j.tust.2024.106105

This paper investigates how Hong Kongers navigate and utilize the corridors, tunnels, and public spaces of the underground transit hubs at Central Station and Hong Kong Station, accommodating over 250,000 passengers daily. Employing participant observation protocols from urban design research, we documented the locations of social activities and surveyed people’s behavior in these underground environments. Despite their grand and efficient engineering, the efficiency of travelators decreases when density exceeds one person per square foot. The narrow widths limit casual conversations. Our findings reveal that people navigate and behave in underground public spaces similarly to above-ground spaces. Additionally, cheaper rents in mezzanine shops and restaurants have enabled these businesses to thrive, benefiting from substantial foot traffic within the transit system. Overall, underground public spaces extend the conventional public realm, catering to diverse social groups and contributing to the vibrancy and vitality of the overall public sphere.

中环站和香港站是香港的地下交通枢纽，每天接待超过 25 万名乘客，本文研究了香港人如何浏览和利用这些交通枢纽的走廊、隧道和公共空间。我们采用城市设计研究中的参与观察规程，记录了社交活动的地点，并调查了人们在这些地下环境中的行为。尽管地下通道工程宏伟而高效，但当密度超过每平方英尺一个人时，其效率就会降低。狭窄的宽度限制了闲聊。我们的研究结果表明，人们在地下公共空间的活动和行为与地面空间类似。此外，由于夹层商店和餐馆的租金较低，这些商业得以蓬勃发展，并从公交系统内的大量人流中获益。总之，地下公共空间扩展了传统的公共领域，满足了不同社会群体的需求，为整个公共领域的活力和生机做出了贡献。

引用次数: 0

New insights into the merging behavior of pedestrians: Quantification of lane competition level 行人并线行为的新见解：车道竞争水平的量化

IF 6.7 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology

Pub Date : 2024-10-09 DOI: 10.1016/j.tust.2024.106126

Merging behavior is a complex crowd movement pattern caused by certain architectural features and has triggered serious crowd accidents worldwide. It is not uncommon to find such merging structures on the floor plans of some underground infrastructures, which deserve more detailed studies on special crowd movement patterns to enhance the safety level. Researchers have demonstrated that the unstable emotions and competitive behaviors of pedestrians during underground evacuation need to be taken into account due to the enclosed environment. However, there are no relevant studies focused on the competition characteristics among pedestrians during the merging process in a quantitative way. The comprehensiveness and systematization of the geometric features of typical merging structures, as well as the way to create the perception of competition and conflict among pedestrians during real emergencies also need to be improved. In this paper, we carried out a series of controlled experiments in symmetric and asymmetric merging structures with different channel widths and incentive levels to investigate crowd merging behavior with competition characteristics. The density and delay time within the merging area, as well as the time interval and lane changes before and after merging are analyzed. By introducing and modifying Simpson’s Diversity Index, we proposed a new method for quantifying the competition level among pedestrians during the merging process. It is concluded that the performance of the merging area is sensitive to the channel widths and the merging layouts, and the negative effect of unbalanced walking priority is more significant under high incentive level. The lane competition level during the merging process is correlated with the effects of various geometric features, movement layouts, and incentive levels. The method proposed in this paper brings new insights into the interpretation of merging scenarios and more studies on pedestrian dynamics. We hope that this study could serve as supplementary work for other modeling or field experiment methods to jointly provide more insights into the crowd management strategies in some underground infrastructures.

合流行为是由某些建筑特征引起的一种复杂的人群流动模式，在世界范围内引发了严重的人群事故。在一些地下基础设施的平面图上，这种合流结构并不少见，值得对特殊的人群流动模式进行更详细的研究，以提高安全水平。研究表明，由于地下环境的封闭性，需要考虑行人在疏散过程中的不稳定情绪和竞争行为。然而，目前还没有相关研究对行人在合流过程中的竞争特征进行定量研究。典型合流结构的几何特征的全面性和系统性，以及在实际紧急情况下创造行人之间竞争和冲突感知的方法也有待改进。在本文中，我们在不同通道宽度和激励水平的对称和非对称合流结构中进行了一系列对照实验，以研究具有竞争特征的人群合流行为。实验分析了并线区域内的密度和延迟时间，以及并线前后的时间间隔和车道变化。通过引入和修改辛普森多样性指数，我们提出了一种新的方法来量化合流过程中行人之间的竞争程度。结论是，合流区的性能对通道宽度和合流布局很敏感，在高激励水平下，不平衡的步行优先权的负面影响更为显著。合流过程中的车道竞争程度与各种几何特征、运动布局和激励水平的影响相关。本文提出的方法为并线场景的解释和更多行人动力学研究带来了新的见解。我们希望这项研究能作为其他建模或现场实验方法的补充工作，共同为一些地下基础设施的人流管理策略提供更多启示。

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引用次数: 0